Instructions to use TeamDelta/Re-ultiima-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeamDelta/Re-ultiima-32B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TeamDelta/Re-ultiima-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TeamDelta/Re-ultiima-32B")
model = AutoModelForCausalLM.from_pretrained("TeamDelta/Re-ultiima-32B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TeamDelta/Re-ultiima-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeamDelta/Re-ultiima-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeamDelta/Re-ultiima-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TeamDelta/Re-ultiima-32B

SGLang

How to use TeamDelta/Re-ultiima-32B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TeamDelta/Re-ultiima-32B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeamDelta/Re-ultiima-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TeamDelta/Re-ultiima-32B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeamDelta/Re-ultiima-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TeamDelta/Re-ultiima-32B with Docker Model Runner:
```
docker model run hf.co/TeamDelta/Re-ultiima-32B
```

Holy-fox commited on Jan 28, 2025

Commit

a4f406e

verified ·

1 Parent(s): cd76c5e

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -8

README.md CHANGED Viewed

@@ -1,24 +1,28 @@
 ---
-base_model: []
 library_name: transformers
 tags:
 - mergekit
 - merge
 ---
-# test_model
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ## Merge Details
 ### Merge Method
-This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen2.5-32B/snapshots/1818d35814b8319459f4bd55ed1ac8709630f003 as a base.
 ### Models Merged
 The following models were included in the merge:
-* /home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen2.5-32B-Instruct/snapshots/5ede1c97bbab6ce5cda5812749b4c0bdf79b18dd
 ### Configuration
@@ -26,16 +30,16 @@ The following YAML configuration was used to produce this model:
 ```yaml
 models:
-  - model: /home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen2.5-32B-Instruct/snapshots/5ede1c97bbab6ce5cda5812749b4c0bdf79b18dd
     parameters:
       weight: 1
       density: 1
 merge_method: ties
-base_model: /home/ubuntu/.cache/huggingface/hub/models--Qwen--Qwen2.5-32B/snapshots/1818d35814b8319459f4bd55ed1ac8709630f003
 parameters:
   weight: 1
   density: 1
   normalize: true
   int8_mask: true
 dtype: float16
-```

 ---
+base_model:
+- Qwen/Qwen2.5-32B
+- Qwen/Qwen2.5-32B-Instruct
 library_name: transformers
 tags:
 - mergekit
 - merge
+license: apache-2.0
+language:
+- en
 ---
+# Re:ultiima-32B
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ## Merge Details
 ### Merge Method
+This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using Qwen/Qwen2.5-32B as a base.
 ### Models Merged
 The following models were included in the merge:
+* Qwen/Qwen2.5-32B-Instruct
 ### Configuration
 ```yaml
 models:
+  - model: Qwen/Qwen2.5-32B-Instruct
     parameters:
       weight: 1
       density: 1
 merge_method: ties
+base_model: Qwen/Qwen2.5-32B
 parameters:
   weight: 1
   density: 1
   normalize: true
   int8_mask: true
 dtype: float16
+```